Setting the file. One moment.
Subchapter 27.66
references/shared/schema-discover-billing.mdMarkdown5 KBView on GitHub
Schema for billing-profile.json, produced by discover-billing.md.
Convention: Values shown as X|Y in examples indicate allowed alternatives — use exactly one value per field, not the literal pipe character.
Cost breakdown derived from GCP billing export CSV. Provides service-level spend and AI signal detection from billing data alone.
{
"metadata": {
"report_date": "2026-02-24",
"project_directory": "/path/to/project",
"billing_source": "gcp-billing-export.csv",
"billing_period": "2026-01"
},
"summary": {
"total_monthly_spend": 2450.00,
"service_count": 8,
"currency": "USD"
},
"services": [
{
"gcp_service": "Cloud Run",
"gcp_service_type": "google_cloud_run_service",
"monthly_cost": 450.00,
"percentage_of_total": 0.18,
"top_skus": [
{
"sku_description": "Cloud Run - CPU Allocation Time",
"monthly_cost": 300.00
},
{
"sku_description": "Cloud Run - Memory Allocation Time",
"monthly_cost": 150.00
}
],
"ai_signals": []
},
{
"gcp_service": "Cloud SQL",
"gcp_service_type": "google_sql_database_instance",
"monthly_cost": 800.00,
"percentage_of_total": 0.33,
"top_skus": [
{
"sku_description": "Cloud SQL for PostgreSQL - DB custom CORE",
"monthly_cost": 500.00
},
{
"sku_description": "Cloud SQL for PostgreSQL - DB custom RAM",
"monthly_cost": 300.00
}
],
"ai_signals": []
},
{
"gcp_service": "Vertex AI",
"gcp_service_type": "google_vertex_ai_endpoint",
"monthly_cost": 600.00,
"percentage_of_total": 0.24,
"top_skus": [
{
"sku_description": "Vertex AI Prediction - Online Prediction",
"monthly_cost": 400.00
},
{
"sku_description": "Generative AI - Gemini Pro Input Tokens",
"monthly_cost": 200.00
}
],
"ai_signals": ["vertex_ai", "generative_ai"]
}
],
"commitments": {
"has_active_cuds": true,
"total_monthly_commitment_fees": 150.00,
"total_monthly_cud_credits": -120.00,
"effective_discount_percent": 8.2,
"details": [
{
"type": "resource_based",
"term": "1_year",
"covered_services": ["Compute Engine"],
"region": "us-central1",
"monthly_fee": 75.00,
"sku_description": "Commitment v1: E2 Cpu in Americas for 1 Year"
},
{
"type": "resource_based",
"term": "1_year",
"covered_services": ["Compute Engine"],
"region": "us-central1",
"monthly_fee": 75.00,
"sku_description": "Commitment v1: E2 Ram in Americas for 1 Year"
}
]
},
"cost_basis": {
"uses_list_price": true,
"total_at_list": 2450.00,
"total_net_of_discounts": 2280.00,
"discount_breakdown": {
"committed_usage_discount": -120.00,
"sustained_usage_discount": -50.00,
"free_tier": 0.00
}
},
"ai_signals": {
"detected": true,
"confidence": 0.85,
"services": ["Vertex AI"]
}
}Key Fields:
summary.total_monthly_spend — Total monthly GCP spend from the billing export (at list price when available)summary.service_count — Number of distinct GCP services with chargesservices[].gcp_service_type — Terraform resource type equivalent for the service (used by downstream phases)services[].monthly_cost — Monthly cost for this service (at list price; excludes commitment fee rows)services[].top_skus — Highest-cost line items within the service (excludes commitment fee SKUs)services[].ai_signals — AI-related keywords found in SKU descriptions for this servicecommitments.has_active_cuds — Whether any CUD commitment fees or credits were detectedcommitments.total_monthly_commitment_fees — Sum of commitment fee line items (positive values)commitments.total_monthly_cud_credits — Sum of CUD credits applied (negative values)commitments.effective_discount_percent — Overall discount rate from all commitmentscommitments.details[] — Individual commitment contracts with type, term, covered services, and monthly feecommitments.details[].type — "resource_based" (vCPU/RAM commitments) or "dollar_based" (spend-based)commitments.details[].term — "1_year" or "3_year"cost_basis.uses_list_price — Whether costAtListUSD was available and used as the baselinecost_basis.total_at_list — Total spend at list price (before discounts)cost_basis.total_net_of_discounts — Total spend after all discounts appliedcost_basis.discount_breakdown — Per-discount-type credit totals (negative values = savings)ai_signals.detected — Whether any AI/ML services were found in the billing dataai_signals.confidence — Confidence that the project uses AI (derived from billing SKU analysis)ai_signals.services — List of AI-related GCP services found